Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses

We present an unsupervised method for inducing semantic frames from verb uses in giga-word corpora. Our semantic frames are verb-specific example-based frames that are distinguished according to their senses. We use the Chinese Restaurant Process to automatically induce these frames from a massive amount of verb instances. In our experiments, we acquire broad-coverage semantic frames from two giga-word corpora, the larger comprising 20 billion words. Our experimental results indicate the effectiveness of our approach.